A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which ‎consume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the ‎efficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with ‎common weights (using multi objective DEA approach) to predict the efficiency of DMUs under mean chance constraints and ‎expected values of the objective functions. In the initial proposed†â€DRF-DEA model, the inputs and outputs are assumed to be ‎characterized by random triangular fuzzy variables with normal distribution, in which data are changing sequentially. Under this ‎assumption, the solution process is very complex. So we then convert the initial proposed DRF-DEA model to its equivalent multi-‎objective stochastic programming, in which the constraints contain the standard normal distribution functions, and the objective ‎functions are the expected values of functions of normal random variables. In order to improve in computational time, we then ‎convert the equivalent multi-objective stochastic model to one objective stochastic model with using fuzzy multiple objectives ‎programming approach. To solve it, we design a new hybrid algorithm by integrating Monte Carlo (MC) simulation and Genetic ‎Algorithm (GA). Since no benchmark is available in the literature, one practical example will be presented. The computational results ‎show that our hybrid algorithm outperforms the hybrid GA algorithm which was proposed by Qin and Liu (2010) in terms of ‎runtime and solution quality. â€

Supplier evaluation and selection in fuzzy environments: a review of MADM approaches

In past years, the multi-attribute decision-making (MADM) approaches have been extensively applied by researchers to the supplier evaluation and selection problem. Many of these studies were performed in an uncertain environment described by fuzzy sets. This study provides a review of applications of MADM approaches for evaluation and selection of suppliers in a fuzzy environment. To this aim, a total of 339 publications were examined, including papers in peer-reviewed journals and reputable conferences and also some book chapters over the period of 2001 to 2016. These publications were extracted from many online databases and classified in some categories and subcategories according to the MADM approaches, and then they were analysed based on the frequency of approaches, number of citations, year of publication, country of origin and publishing journals. The results of this study show that the AHP and TOPSIS methods are the most popular approaches. Moreover, China and Taiwan are the top countries in terms of number of publications and number of citations, respectively. The top three journals with highest number of publications were: Expert Systems with Applications, International Journal of Production Research and The International Journal of Advanced Manufacturing Technology

An Integrated Fuzzy Multi-Criteria Decision Making Method For Supplier Evaluation

This research investigates the risk exposure arising from the supplier evaluation criteria of cost, quality, delivery, and flexibility of the supplier. Penyelidikan ini bertujuan untuk mengkaji risiko yang timbul daripada kos, kualiti, penghantaran dan fleksibiliti bagi penilaian pembekal

An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM

Robust approach to DEA technique for supplier selection problem: A case study at Supplying Automotive Parts Company (SAPCO)

Abstract In many industries such as automotive industry, there are a lot of suppliers dealing with the final products manufacturer. With growing numbers of suppliers, the suppliers' efficiency measurement often becomes the most significant concern for manufacturers. Therefore, various performance measurement models such as DEA, AHP, TOPSIS, are developed to support supplier selection decisions. After an exhaustive review of the supplier selection methods, we employ data envelopment analysis (DEA) for computing the relative efficiency of the suppliers and introducing the most efficient supplier as a benchmark. In reality, there are large amounts of uncertainty regarding the suppliers' measurements; therefore, we propose the robust optimization approach to the real application of DEA (RDEA). In this approach, uncertainties about incomes and outcomes of decision making units (DMUs) are involved in the relative suppliers' efficiencies. The proposed RDEA approach is utilized for the selection of suppliers which manufacture the automotive safety components in Supplying Automotive Parts Company (SAPCO), an Iranian leading automotive enterprise. Numerical example will illustrate how our proposed approach can be used in the real supplier selection problem when considerable uncertainty exists regarding the suppliers' input and output dat

Technology Decisions In New Product Development

Selecting the right technology is one of the most important and challenging decisions in new product development (NPD) process that can greatly affect all downstream design activities which have a great impact on product success in the market place. Although new technologies may bring more competitive advantage by offering higher performance to price, they also make the NPD process more risky and challenging. We model the technology selection problem of a firm that is defining its new product in the presence of the technology uncertainty. At each review stage, firm has the options: select and commit to any technology alternatives or postpone this decision to the next review stage in order to gather more information. Delays in making technology decisions are likely to increase NPD cycle time by shifting forward downstream activities and ultimately may impose an increased development cost and profit loss for the firm. Our Analysis describes the optimal strategies for this problem and investigate the impact of technological and market uncertainty as well as time trade-offs on technology selection problem in NPD

Developing Talent from a Supply-Demand Perspective: An Optimization Model for Managers

While executives emphasize that human resources (HR) are a firm's biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

The Encyclopedia of Neutrosophic Researchers, 5th Volume

Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. In the past years the fields of neutrosophics have been extended and applied in various fields, such as: artificial intelligence, data mining, soft computing, decision making in incomplete / indeterminate / inconsistent information systems, image processing, computational modelling, robotics, medical diagnosis, biomedical engineering, investment problems, economic forecasting, social science, humanistic and practical achievements. There are about 7,000 neutrosophic researchers, within 89 countries around the globe, that have produced about 4,000 publications and tenths of PhD and MSc theses, within more than two decades. This is the fifth volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editor’s invitation, with an introduction contains a short history of neutrosophics, together with links to the main papers and books